MSC – DATA SCIENCE 
                                                  The American College 
                                (An Autonomous Institution Affiliated to Madurai Kamaraj University) 
                         (Re-accredited [2nd Cycle] by NAAC with Grade „A‟ & CGPA of 3.46 on a 4 point scale) 
                                                         Madurai  
                                                Proposed PG Grid for June 2020 
                  Sem.     Course Code                     Course Title                   Hr.   Cr.      Mark 
                  I      PDS 1501          Concepts of Data Science                        5      5       75 
                         PDS 1503          Data Analytics (T + L)                          6      5       75 
                         PDS 1505          Artificial Intelligence                         5      5       75 
                         PDS 1607          Python Programming                              5      6       80 
                         PDS 1409          Python Programming Lab                          4      4       60 
                         PDS 1511          Probability and Statistics                      5      5       75 
                         Total                                                            30     30       420 
                  II     PDS 1502          Data Mining and Warehousing                     5      5       60 
                         PDS 1404          Big Data Analytics                              4      4       60 
                         PDS 1406          Big Data Analytics Lab                          4      4       80 
                         PDS 1408          Machine Learning                                4      4       60 
                         PDS 1410          Computer Vision                                 4      4       80 
                         PDS 1512          Linear Algebra                                  5      5       80 
                         PDS 1414          Elective I                                      4      4       60 
                         Total                                                            30     30       420 
                  III    PDS 2501          Natural Language Processing (T + L)             5      5       60 
                         PDS 2403          Deep Learning                                   4      4       80 
                         PDS 2405          Reinforcement learning                          4      4       80 
                         PDS 2507          Operation Research                              5      5       80 
                         PDS 2409          Effective Communications                        4      4       80 
                         PDS 2411          Elective II                                     4      4       60 
                         PDS 2413          Mini Project Lab                                4      4       40 
                         Total                                                            30     30       480 
                  IV     PDS 2302          Industry Project                               30     30       480 
                         Total                                                            30     24       480 
                         GRAND                                                            120    90      1800 
                         TOTAL 
                                                                 
                                                              
                                                              
                                                              
                                                              
                            
                   Concepts of Data Science 
        Unit I : Introduction 
        Benefits and uses of data science - Facets of data - The big data ecosystem and data 
        science - data science process. 
        Unit II: Machine Learning 
        What is machine learning and why should you care about it? - The modeling process - 
        Types of machine learning. Handling large data on a single computer: General techniques 
        for handling large volumes of data - General programming tips for dealing with large data 
        sets - Case study 1: Predicting malicious URLs. 
        Unit III: Big Data 
        Distributing data storage and processing with frameworks - Case study: Assessing risk 
        when loaning money - Introduction to NoSQL - Case study: What disease is that? - 
        Introducing  connected  data  and  graph  databases  -  Connected  data  example:  a  recipe 
        recommendation engine.  
        Unit IV: Text mining and text analytics 
        Text mining in the real world - Text mining techniques: Bag of words - Stemming and 
        lemmatization - Decision tree classifier - Case study: Classifying Reddit posts 
        Unit V: Data visualization to the end user 
        Data visualization options - Crossfilter, the JavaScript MapReduce library - Creating an 
        interactive dashboard with dc.js - Dashboard development tools 
        Text Book: 
        1. Davy Cielen, Arno D. B. Meysman, Mohamed Ali, Introducing Data Science, Manning 
        Publications Co, 2016. 
        Reference Book: 
        1. John D. Kelleher and Brendan Tierney, “Data Science”, First Edition, The MIT Press, 
        London, 2018. 
        2.  Lillian  Pierson,  “Data  Science  for  Dummies”,  2nd  Edition,  John  Wiley  &  Sons 
        publications, 2017. 
        3.  EMC  Education  Services,  Data  Science  &  Big  Data  Analytics:  Discovering, 
        Analyzing, Visualizing and Presenting Data, John Wiley & Sons, Inc, 2015. 
                                                                                     
                         4.  Trevor  Hastie,  Robert  Tibshirani,  Jerome  Friedman,  The  Elements  of  Statistical 
                         Learning Data Mining, Inference, and Prediction, Second Edition, Springer, 2017. 
                                                                                      
                                                                       Data Analytics 
                         Course Objectives: 
                                  To develop problem solving abilities using Mathematics 
                                  To apply algorithmic strategies while solving problems 
                                  To develop time and space efficient algorithms 
                                  To study algorithmic examples in distributed, concurrent and parallel 
                                   environments 
                         Course Outcomes: 
                         On completion of the course, student will be able to– 
                                  To write case studies in Business Analytic and Intelligence using mathematical 
                                   models. 
                                  To present a survey on applications for Business Analytic and Intelligence. 
                                  To write problem solutions for multi-core or distributed, concurrent/Parallel 
                                   environments 
                         Unit I: Data Analytics Overview 
                         Introduction – Importance- Types of Data Analytics – data analytics life style: overview – 
                         discovery- data preparation – model planning – model building – communicate result - 
                         Operationalize. Case study: Global Innovation Network and Analysis (GINA). 
                         Unit II: Statistics for Analytics 
                         Statistical Methods for Evaluation: Hypothesis Testing - Difference of Means- Wilcoxon 
                         Rank-Sum Test - Type I and Type II Errors – Power and Sample Size - ANOVA 
                         Unit III: Time Series & Text Analysis 
                         Overview  of  Time  Series  Analysis:  -  Box-Jenkins  Methodology  -  ARIMA  Model  - 
                         Additional Methods. Text Analysis: Text Analysis Steps – A Text Analysis Example - 
                         Collecting  Raw  Text  -  Representing  Text  -  Term  Frequency-Inverse  Document 
                            
        Frequency  (TFIDF  -  Categorizing  Documents  by  Topics  -  Determining  Sentiments  - 
        Gaining Insights. 
        Unit IV: Supervised Learning 
        Introduction - Variable Types and Terminology - Least Squares and Nearest Neighbors - 
        Statistical  Decision  Theory  -  Structured  Regression  Models  -  Classes  of  Restricted 
        Estimators. Support Vector Machines and Flexible Discriminants: The Support Vector 
        Classifier  -  Support  Vector  Machines  and  Kernels.  Prototype  Methods  and  Nearest-
        Neighbors: Prototype Methods 
        Unit V: Unsupervised Learning 
        Introduction  -  Association  Rules  -  Cluster  Analysis  -  Random  Forests:  Definition  of 
        Random Forests - Details of Random Forests - Analysis of Random Forests - Undirected 
        Graphical Models - Markov Graphs and Their Properties - Undirected Graphical Models 
        for Continuous Variables - Undirected Graphical Models for Discrete Variables. 
        Text Book 
        1. EMC Education Services, Data Science & Big Data Analytics: Discovering, 
        Analyzing, Visualizing and Presenting Data, John Wiley & Sons, Inc, 2015. 
        2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical 
        Learning Data Mining, Inference, and Prediction, Second Edition, Springer, 2017. 
        Uint I (Text Book 1): Chapter 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8,  
        Unit II (Text Book 1): Chapter 3.3. 
        Unit III (Text Book 1): Chapter 8.1, 8.2, 8.3, 9.1 – 9.8. 
        Unit IV (Text Book 2): Chapter 2.1, 2.2, 2.3, 2.4, 2.7, 2.8, 12.2, 12.3, 13.2. 
        Unit V (Text Book 2): 14.1, 14.2, 14.3, 15.2, 15.3, 15.4, 17.2, 17.3, 17.4 
        Reference Book: 
        1. Anil Maheshwari, Data Analytics, McGraw Hill Education; First edition, 2017. 
        2. Annalyn Ng, Data Science for the Layman, Shroff Publishers; First edition, 2018. 
        3. Ramesh Sharda, Dursun Delen, Efraim Turban, Business Intelligence, Analytics, and 
        Data Science: A Managerial Perspective, Pearson Education, Fourth edition, 2019.